Combining Local and Global Viewpoint Planning for Fruit Coverage

TL;DR

This paper introduces a combined local and global viewpoint planning approach to improve fruit coverage in 3D sensing, effectively reducing occlusions and increasing coverage in complex plant environments.

Contribution

It proposes a novel method that integrates local occlusion avoidance with global coverage planning for better sensing of occluded plant parts.

Findings

Significantly increased coverage of regions of interest.

Effective occlusion avoidance in complex plant structures.

Improved sensing performance in simulated experiments.

Abstract

Obtaining 3D sensor data of complete plants or plant parts (e.g., the crop or fruit) is difficult due to their complex structure and a high degree of occlusion. However, especially for the estimation of the position and size of fruits, it is necessary to avoid occlusions as much as possible and acquire sensor information of the relevant parts. Global viewpoint planners exist that suggest a series of viewpoints to cover the regions of interest up to a certain degree, but they usually prioritize global coverage and do not emphasize the avoidance of local occlusions. On the other hand, there are approaches that aim at avoiding local occlusions, but they cannot be used in larger environments since they only reach a local maximum of coverage. In this paper, we therefore propose to combine a local, gradient-based method with global viewpoint planning to enable local occlusion avoidance while still being able to cover large areas. Our simulated experiments with a robotic arm equipped with a camera array as well as an RGB-D camera show that this combination leads to a significantly increased coverage of the regions of interest compared to just applying global coverage planning.